Deer activity levels and patterns vary along gradients of food availability and anthropogenic development

Animal activity reflects behavioral decisions that depend upon environmental context. Prior studies typically estimated activity distributions within few areas, which has limited quantitative assessment of activity changes across environmental gradients. We examined relationships between two response variables, activity level (fraction of each day spent active) and pattern (distribution of activity across a diel cycle) of white-tailed deer (Odocoileus virginianus), with four predictors—deer density, anthropogenic development, and food availability from woody twigs and agriculture. We estimated activity levels and patterns with cameras in 48 different 10.36-km2 landscapes across three larger regions. Activity levels increased with greater building density, likely due to heightened anthropogenic disturbance, but did not vary with food availability. In contrast, activity patterns responded to an interaction between twigs and agriculture, consistent with a functional response in habitat use. When agricultural land was limited, greater woody twig density was associated with reduced activity during night and evening. When agricultural land was plentiful, greater woody twig density was associated with more pronounced activity during night and evening. The region with the highest activity level also experienced the most deer-vehicle collisions. We highlight how studies of spatial variation in activity expand ecological insights on context-dependent constraints that affect wildlife behavior.

comparisons of two activity distributions 11,12 .Because deer can exhibit behavioral fluidity in response to environmental context, additional plasticity in activity might be expected.We sought quantitative understanding of how deer activity levels and patterns change across gradients of various environmental characteristics during the winter in the midwestern USA (henceforth, Midwest).
Frequent disturbances may generally cause higher activity levels due to avoidance of perceived danger.Disturbances from human activities can influence numerous deer behaviors [13][14][15] .Human development in the form of roads and buildings may specifically affect deer activity because development is consistently associated with mortality from deer-vehicle collisions 16 .Therefore, deer may change their activity along gradients of human activity or development.
Deer in the Midwest rely on several food resources during winter including low-quality woody twigs in forested sites and higher-quality crop residue from agricultural fields after harvest.But deer also must balance food acquisition with predation risk 17 .According to foraging theory, deer should bias feeding activity in favor of safer areas and either exhibit greater per capita vigilance or form larger groups in riskier areas 18 .Fear of predation in deer may be associated with time of day and vegetative concealment (i.e., how much vegetation is obstructing direct line of sight to deer 19 ).Within home ranges, disproportionate changes in habitat use with availability (i.e., functional responses in habitat use 20 ) may result from attempts by individuals to balance decisions about activity with spatially varying risks and rewards 21 .In Midwest landscapes, deer use of crop residue in open agricultural fields and woody twigs in forest patches may reflect attempts to balance tradeoffs of forage quality, quantity, or perceived predation risk.If so, interactions between the amount of agriculture and woody twigs may be important predictors of deer activity.
Density dependence in ungulates is of interest ecologically and because of its implications for management of game species 10,22 .Theory suggests that larger densities of ungulates can decrease the amount of available per capita food and thereby increase intraspecific competition for nutrients 23 .Density-dependent responses in activity of wild boar (Sus scrofa), red deer (Cervus elaphus), and roe deer (Capreolus capreolus) have been documented 24 .Moreover, elk (Cervus canadensis) exhibited density-dependent habitat selection 25 .But no quantitative assessment has examined how deer activity shifts as a function of population density.

Hypotheses and predictions
Our goals were to determine whether and to what extent deer winter activity levels and patterns vary spatially, and to discover if variables related to anthropogenic disturbances, food, and intraspecific density correspond to these activity changes.First, we hypothesized that deer would alter their activity levels in response to human development.We predicted that road length and density of buildings would exhibit positive relationships with deer activity levels.
Secondly, we hypothesized that deer activity levels would respond to intraspecific density.Greater density, by decreasing available per capita food, may increase the amount of time spent foraging 23 ; accordingly, we predicted a positive relationship between activity levels and intraspecific density of deer.
Thirdly, we hypothesized that deer activity levels would respond to food availability.We predicted that deer activity levels would decrease as the density of woody twigs and the amount of agricultural land increased, because more available food should decrease the time required for foraging.Further, we predicted a significant interactive effect of woody twigs and agricultural land due to activity levels of deer requiring tradeoffs between resource acquisition and safety.
Fourthly, we hypothesized that deer activity patterns would respond to availability of different food types in a manner that incorporated time-dependent tradeoffs between forage acquisition and safety.Deer are crepuscular 26 , predominantly consuming agricultural products in open fields during nocturnal hours but consuming woody twigs during any time of day 27 .Moreover, deer may prefer diverse diets which can ensure proper nutrient intake and avoidance of overconsumption of any single mineral 28,29 .Therefore, in areas with small amounts of agricultural land, we predicted deer would alter their activity patterns to be more active at night in areas where woody twig density was low, because deer would rely more heavily on crops as a food source at night.In contrast, we predicted deer would be less active at night in areas where woody twig density was high, because deer would be able to consume adequate amounts of twigs during daytime in the relative safety of woodlands.However, in areas with plentiful amounts of agricultural land, we predicted deer would be more active during daytime in areas where woody twig density was low, because deer would need to search for and consume scarce woody twigs throughout daytime.In areas with plentiful agricultural land, we also predicted deer would be more nocturnal in areas where woody twig density was high, because woody twigs would be readily available for consumption during daytime and thus deer could concentrate their time spent active during nocturnal hours when crop consumption could more safely occur.

Results
We sampled 1,018 unique locations with camera traps (total trap nights = 13,816).Cameras did not detect deer at 187 locations.The average number of sampling locations and deer detections used for activity estimation was 21 and 536, respectively, per replicate 10.36-km 2 landscape.We detected > 100 deer detections in 42 different landscapes.
Deer spent more time active during morning and less at night in RMU 9, but the reverse was true for RMU 4 (Fig. 2).Deer allotted their activity more evenly throughout each of the four time-of-day categories in RMU 3 when compared to the other regions.Deer devoted similar fractions of total activity to evening hours in all three regions.www.nature.com/scientificreports/

Replicate landscape analysis
Activity levels Models containing predictors associated with anthropogenic development, twig density, and intraspecific density had evidence ratios < 3; neither area of agriculture nor models with interactions received support (Table 1, Table S1).The model containing number of buildings had the most support; in it, the number of buildings exhibited a strong positive relationship with deer activity levels (Table 2, Fig. 3).

Activity patterns
The only model with support for activity patterns contained a strong interaction between twig density and the amount of surrounding land devoted to agriculture (Tables 1, 3).Specifically, when the amount of agricultural land was small, increases in woody twig density were associated with reduced deer activity during night and evening periods, and greater relative activity during morning and (moderately) during daytime periods (Fig. 4).Conversely, when the amount of agricultural land was large, increases in woody twig density were associated  www.nature.com/scientificreports/with greater deer activity during night and evening periods, and reduced activity during morning and daytime periods (Fig. 4).We provide plots of posterior distributions, posterior predictive checks, and trace plots in Supplementary Figs.S1-S4.

Discussion
Activity patterns of deer responded to the interaction of natural and agricultural food sources, supporting our prediction.Other studies have demonstrated functional responses in habitat use by ungulates consistent with modulation of site-specific differences in risk and reward 30,31 .But our results are, to our knowledge, the first to   document such modulation operating across landscapes in a manner that shifts the time of day during which activity occurs.In contrast to our prediction, we found no strong relationships between deer activity levels and these same sources of foods.Although we tested a large gradient of woody twig density and agricultural availability, food in our study areas may not have been limited enough to alter both activity levels and patterns simultaneously.Instead of changing activity levels, deer appeared to obtain sufficient amounts of both agriculturally and naturally sourced foods while adjusting their activity patterns based upon the availability of each food source.
Although previous research documented activity levels to be density dependent in other ungulates 24 , we found no strong relationship between deer density and deer activity levels.Limitation of food resources is a major contributor to intraspecific competition and ecological carrying capacity for deer 32 .But other research found minimal effects of deer use intensity (an index of population density) on the health of forest understories in the same areas and times we deployed camera traps 33 .Vast amounts of agricultural food are present in RMUs 3 and 9, and forests containing woody food are widespread in RMU 4. Therefore, plentiful food resources may have reduced or eliminated any potential intraspecific competition for food within our study system and thus negated our ability to detect any potential response in deer activity levels to food.
Deer activity may be more broadly related to other areas of interest in wildlife management.For instance, deer-vehicle collisions are a major concern of wildlife managers 34 .Population density has been a key focus of those attempting to reduce or model animal-vehicle collisions [35][36][37] , but animal activity may also play a role 38,39 .If animals either are active during a greater fraction of each day or shift their activity to coincide with periods of peak vehicular traffic volume, the chances of animals and vehicles colliding on the landscape likely will increase.In our study, we documented in RMU 9 the highest regional activity levels and a pattern characterized by a greater fraction of activity during the morning rush hours.Under such conditions, accidents involving collisions between motorists and deer might be expected.Indeed, deer-vehicle collisions occur at a rate 1.98 times higher in RMU 9 compared to RMU 3 and 4 40 .Therefore, quantitative examinations of the relationships between characteristics of activity distributions and deer-vehicle collisions may help future management planning to reduce collisions.If positive relationships are found, incentivizing humans to hunt deer in close proximity to roadways may reduce occurrence of deer-vehicle collisions by causing deer to shift to nocturnal activity patterns, reduce movement rates, or select areas further from roads 41,42 .
Metrics from estimated animal activity distributions are fundamental parameters used for research purposes in energetics 11 , population ecology 43 , movement ecology 44 , species interactions 11 , and urban ecology 45 .In population estimation, activity levels are often used to estimate the sampling availability (i.e., fraction of the 24-h day that animals are moving and thus able to be detected by camera traps) of the target species for camera trapping 38,46 .In so doing, most studies have used a single estimate of animal activity to correct for the sampling availability of the target population.However, our results suggest that models of variation in activity level across study regions may yield more accurate estimates of sampling availability.Improvements may be especially dramatic for spatially explicit models of density that depict how the size of animal populations change across space while also relying on estimates of sampling availability.We encourage future research in population ecology to consider how sampling availability changes across space.
Quantitative modelling of spatial variation in animal activity metrics requires sampling intensity sufficient to estimate activity distributions within many spatial replicates.Such modeling has the capacity to enhance our understanding of animal behavior, and to improve knowledge useful for wildlife management and conservation.The sampling effort required for modeling of animal activity poses both logistical and financial challenges.Fortunately, large-scale camera trapping operations are becoming more common in wildlife research and management [47][48][49] and should enable closer inspection of environmental and human factors affecting animal activity.Moreover, opportunities to integrate other data sources (e.g., bio logging 50 ) should improve our ability to detect shifts and test for effects of risk:reward tradeoffs on activity levels and patterns across environmental gradients.

Study sites
We deployed camera traps within Regional Management Units (RMU) 3, 4, and 9 in Indiana, USA 40 .We used the same sampling design and camera deployment strategy outlined in previous research within the same study areas 51 .We placed cameras in random 10.36-km 2 sub-areas (henceforth, landscapes).We sampled 20 landscapes across all three RMUs in each year.We repetitively sampled two landscapes in each RMU during each year to examine annual differences unrelated to spatial variation.Thus 48 unique landscapes were sampled.A thorough description of each RMU is presented in previous research 52 .

Estimation of activity levels and patterns
Times of day when animals are active and inactive are associated with greater and lesser numbers of captures by camera traps, respectively 54 .The distribution of detection times can thus be used to estimate animal activity level.
To do this, we first double-anchored detection times of deer based on the average sunrise and sunset times at camera trap locations on days we collected data using methods of previous research 1 .Double anchoring detection times accounts for the differing sunset and sunrise times across the days and locations that we sampled, which is critical because deer are crepuscular 26 .We then fit circular kernel probability density functions to the doubleanchored detection times of deer using the methods of previous research 53 .We used the area under each circular kernel probability density function as the estimate of deer activity level, and used nonparametric bootstrapping to estimate uncertainty associated with activity levels 54 .We used the "activity" package in R to double anchor detection times, fit probability density functions, and estimate activity levels 55,56 .
We estimated activity patterns by first defining four different time-of-day categories: night (defined as > 2 h after sunset to > 2 h before sunrise), morning (defined as < 2 h before sunrise to < 2 h after sunrise), daytime (defined as > 2 h after sunrise to > 2 h before sunset), and evening (defined as < 2 h before sunset to < 2 h after sunset).We then used numerical integration to estimate what fraction of the daily deer activity level that occurred within each time-of-day category by where t 1 and t 2 are the two temporal bounds of a given time-of-day category, and f (x) is the circular kernel density used to model activity level.

Deer density
We estimated spatially explicit deer density using the density surface model from a previous project in the same study areas and times this study was conducted 51 .These density estimates utilized camera trap distance sampling and landscape covariates within a generalized additive model to predict deer density inside 30 × 30-m cells across each of the RMUs we sampled 51 .To estimate deer density within each landscape, we averaged the density estimate of each cell within the landscape of interest.Density estimates within landscapes ranged from 0.78 to 37.43 deer/km 2 (mean = 9.06, SE = 1.08).

Anthropogenic development
We calculated the total road length and number of buildings within landscapes using the Indiana primary and secondary roads state-based shapefile 57 and the US building footprints shapefile from Microsoft (https:// github.com/ Micro soft/ USBui lding Footp rints), respectively.

Food availability
We calculated the total amount of agricultural land within landscapes using the National Land Cover Database 2019 landcover raster file 53 .We also estimated the density of non-avoided twigs as an index of natural food using the methods and data from previous research in the same study areas 58 .Specifically, we sampled five 1-m 2 quadrats placed every 10 m along randomly placed and oriented 50-m transects.The number of transects sampled per forest patch was determined by A i /2NT i < NT i , where NT i = the number of transects in forest patch i , and A i = the area (ha) of forest patch i .We counted all living woody twigs in 3-dimensional space 20-180 cm above the quadrat 59 , and estimated twig density ( D i ) by D i = t i /n i , where t i = the total number of twigs counted in forest patch i , and n i = the total number of quadrats surveyed within forest patch i .We used a Pearson's chi- square test for count data 60 to classify twig species as non-avoided, which we defined as significantly (consumed at a higher rate than expected; α ≤ 0.05) or neutrally (consumed at a similar rate than expected; α > 0.05) selected for consumption.

Natural predators and human hunting
Deer will often alter their decision making when they co-occur with predators or perceived predators 61,62 .Although coyotes (Canis latrans) predominantly prey solely on juvenile deer 63 , past research has found behavioral responses in deer when exposed to coyotes 64 .However, during times of the year when juvenile deer are extremely young, past research found only groups containing juveniles to respond to coyote presence by altering their activity pattern 11 .Groups solely containing older deer did not alter their activity patterns when subjected to the presence of coyotes 11 .Our study was conducted later in the year when juvenile deer are much larger and thus less susceptible to predation by coyotes.For these reasons, we did not consider coyotes as a predictor of deer activity patterns in this work.
Human hunting is a major source of deer mortality 16 and can affect several deer behaviors 41,42 .However, outside of times of year or areas where humans hunt deer, deer often become accustomed to nonhunting humans (e.g., human walking or dog walking) and often do not perceive humans directly as risky 19,65 .Our sampling did not occur during seasons when humans could harvest deer in Indiana.For these reasons, we did not test the effects of indices of human hunters on deer activity levels or patterns.

Regional analysis
We used a Wald χ 2 statistic with 1 degree of freedom 54 to test for differences between (1) activity levels within regions, (2) fraction of deer activity levels exerted within each time-of-day category between RMUs, and (3) fraction of deer activity levels exerted between each time-of-day category within RMUs.In each Wald test, W = where AL 1 and AL 2 are the two activity levels being compared and SE denotes the standard error.To minimize potential for Type 1 Error due to multiple pairwise comparisons, we implemented a Holm's adjustment strategy 66 .

Replicate landscape analysis
We modelled activity levels of deer within 10.36-km 2 landscapes using a mixed effects beta regression.We used a beta regression because activity level is a fraction bounded between 0 and 1.We modelled the fraction of deer activity levels exerted during each of the four time-of-day categories using a mixed effects Dirichlet regression.We used a Dirichlet regression because the four fractions of deer activity levels exerted during each of the timeof-day categories are naturally compositional and thus sum to 1.We did not include activity levels or patterns from landscapes that had < 100 deer detections, as other research suggested that coefficients of variation exceed 0.10 with sample sizes of < 100 and thus are imprecise 54 .
We fit all models within a hierarchical Bayesian framework in R using the "brms" package 67 .Because landscapes were naturally nested within larger regions, we included a random intercept for region.For all continuous predictors, we subtracted the mean and divided by the standard deviation to aid convergence.We ran 3 Markov chains for a total of 2500 iterations per chain.We discarded the first 1000 iterations per chain and specified Student-T priors for all regression coefficients and random effects in the beta and Dirichlet models (degrees of freedom = 3, mean = 0, standard deviation = 2.5; default in "brms" package).
To examine support for each of the models, we used weights based on approximate leave-one-out cross validation 68 and evidence ratios of model weights (i.e., the weight of the top model divided by the weight of the model under consideration).We considered models with evidence ratios < 3 to have statistical support for being the "best" model in the candidate set, and only further reported statistics on regression coefficients from these models.Because 89% confidence intervals are more stable when posterior samples are < 10,000, we considered strong predictors those with associated 89% credible intervals that did not overlap zero 69 .

Figure 1 .
Figure1.Landcover types and sampled landscapes within Regional Management Units (RMU) 3 (west central), 4 (southern), and 9 (northeastern, two separate areas) within Indiana, USA.Within each RMU, we estimated the activity distributions of white-tailed deer using camera traps deployed within landscapes during the winters from 2019 to 2021.

Figure 2 .
Figure 2. The fraction of deer activity levels (± 95% confidence intervals) observed during morning, daytime, evening, and night within Regional Management Units (RMU) 3, 4, and 9 in Indiana, USA (A).Fractions of deer activity levels observed between RMUs and within times of day (B) or within RMUs and between times of day (C) are similar (P > 0.05) if sharing an identically colored line.We collected activity data using camera traps during the winters of 2019 to 2021.

Figure 3 .
Figure 3. Effects plot from a hierarchical Bayesian beta regression model predicting white-tailed deer activity levels (± standard error) as a function of the number of buildings within 3.2 × 3.2-km landscapes.Activity data were collected using camera traps during the winters of 2019 to 2021 in Indiana, USA.

Figure 4 .
Figure 4. Effects plot from a hierarchical Bayesian Dirichlet model regressing the fraction of white-tailed deer activity levels exerted during night (baseline), morning, daytime, and evening (± standard error, SE) across an interaction between the amount of land used for agriculture (0.13, 0.42, and 0.72 km 2 = − 1, mean, and 1 standard deviation, respectively) within the 3.2 × 3.2-km landscape and the density of woody twigs within the same landscape (twigs/m 2 ).Activity data were collected using camera traps during the winters of 2019 to 2021 in Indiana, USA.

Table 1 .
Support for models regressing white-tailed deer activity levels (Response = Level) and the fraction of activity level exerted during different times of day (Response = Pattern).Model weights were based on approximate leave-one-out cross validation, and evidence ratios (i.e., the weight of the top model divided by the weight of the model under consideration) were computed using model weights.Weights for different responses were computed separately.Both deer activity metrics were estimated from camera trap data collected in Indiana during the winters of 2019 to 2021.Hypothesis = the general a priori reasoning for expecting an effect on activity.Predictor = the covariates contained within the model being considered.Buildings = the number of buildings in the landscape (i.e., 3.2 × 3.2-km area in which camera traps were deployed).Roads = the length of roads within the landscape.Twigs = the density of non-avoided woody twigs in the landscape.Agriculture = the amount of land used for agriculture in the landscape.Deer density = the average density of deer in the landscape."*" denotes an interactive and additive effect.Vol.:(0123456789) Scientific Reports | (2024) 14:10223 | https://doi.org/10.1038/s41598-024-60079-6

Table 2 .
Estimates and 89% lower (LCI) and upper (UCI) confidence intervals for regression coefficients from all beta regression models of white-tailed deer activity levels across various predictors.Only shown are models exhibiting evidence ratios < 3. Model form denotes the predictors used to fit the model.Predictors are defined in Table1.Activity data were collected using camera traps during the winters of 2019 to 2021 in Indiana, USA.

Table 3 .
Estimates and 89% lower (LCI) and upper (UCI) confidence intervals for coefficients from a Dirichlet regression modeling the fraction of white-tailed deer activity levels exerted during night (baseline), morning, daytime, and evening across various predictors.Only one model is shown; no other models exhibited an evidence ratio < 3. Predictors are defined in Table1.Activity data were collected using camera traps during the winters of 2019 to 2021 in Indiana, USA.Night was used as the baseline.